QDL: Quantitative Dynamic Latency Model in Permissioned Blockchain System

Latency, as a critical attribute influencing scalability and consistency in blockchain systems, has attracted significant research attention. This paper addresses the issue of latency evaluation by presenting a novel framework. We thoroughly analyze the factors affecting latency in blockchain system, including concurrency, consistency, and contention. To accurately model the correlation between latency and the quantity of nodes in permissioned blockchain setup, we introduce the Latency Regression Loss (LRL) function. By utilizing Newton’s interpolation and least squares methods, we determine the hyperparameters. Subsequently, we devise the framework for the Quantitative Dynamic Latency (QDL) evaluation model, which accounts for both the growth rate and magnitude of latency. Utilizing the parameters derived from the LRL function facilitated the calculation of latency growth rate, allowing for a comprehensive evaluation of latency. Finally, we validated the accuracy of the LRL function through extensive experiments and simulated the adaptability of the QDL model in latency assessment.